| language: en | |
| license: mit | |
| datasets: | |
| - ronig/protein_binding_sequences | |
| ## Peptriever BiEncoder for Protein-Peptide Binding | |
| The model and training process is outlined in [this application note](). Training code can be found [here](https://github.com/RoniGurvich/Peptriever). | |
| For more details see the [application page](https://peptriever.app) | |
| ## Usage | |
| ```python | |
| import torch | |
| from transformers import AutoModel, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("ronig/protein_biencoder") | |
| model = AutoModel.from_pretrained("ronig/protein_biencoder", trust_remote_code=True) | |
| model.eval() | |
| peptide_sequence = "AAA" | |
| protein_sequence = "MMM" | |
| encoded_peptide = tokenizer.encode_plus(peptide_sequence, return_tensors='pt') | |
| encoded_protein = tokenizer.encode_plus(protein_sequence, return_tensors='pt') | |
| with torch.no_grad(): | |
| peptide_output = model.forward1(encoded_peptide) | |
| protein_output = model.forward2(encoded_protein) | |
| print("distance: ", torch.norm(peptide_output - protein_output, p=2)) | |
| ``` | |
| ## Version | |
| Model checkpint: `peptriever_2023-06-23T16:07:24.508460` |